2,616 research outputs found
TDMA Achieves the Optimal Diversity Gain in Relay-Assisted Cellular Networks
In multi-access wireless networks, transmission scheduling is a key component
that determines the efficiency and fairness of wireless spectrum allocation. At
one extreme, greedy opportunistic scheduling that allocates airtime to the user
with the largest instantaneous channel gain achieves the optimal spectrum
efficiency and transmission reliability but the poorest user-level fairness. At
the other extreme, fixed TDMA scheduling achieves the fairest airtime
allocation but the lowest spectrum efficiency and transmission reliability. To
balance the two competing objectives, extensive research efforts have been
spent on designing opportunistic scheduling schemes that reach certain tradeoff
points between the two extremes. In this paper and in contrast to the
conventional wisdom, we find that in relay-assisted cellular networks, fixed
TDMA achieves the same optimal diversity gain as greedy opportunistic
scheduling. In addition, by incorporating very limited opportunism, a simple
relaxed-TDMA scheme asymptotically achieves the same optimal system reliability
in terms of outage probability as greedy opportunistic scheduling. This reveals
a surprising fact: transmission reliability and user fairness are no longer
contradicting each other in relay-assisted systems. They can be both achieved
by the simple TDMA schemes. For practical implementations, we further propose a
fully distributed algorithm to implement the relaxed-TDMA scheme. Our results
here may find applications in the design of next-generation wireless
communication systems with relay architectures such as LTE-advanced and WiMAX.Comment: 26 pages, 8 figure
Graphical Methods for Defense Against False-data Injection Attacks on Power System State Estimation
The normal operation of power system relies on accurate state estimation that
faithfully reflects the physical aspects of the electrical power grids.
However, recent research shows that carefully synthesized false-data injection
attacks can bypass the security system and introduce arbitrary errors to state
estimates. In this paper, we use graphical methods to study defending
mechanisms against false-data injection attacks on power system state
estimation. By securing carefully selected meter measurements, no false data
injection attack can be launched to compromise any set of state variables. We
characterize the optimal protection problem, which protects the state variables
with minimum number of measurements, as a variant Steiner tree problem in a
graph. Based on the graphical characterization, we propose both exact and
reduced-complexity approximation algorithms. In particular, we show that the
proposed tree-pruning based approximation algorithm significantly reduces
computational complexity, while yielding negligible performance degradation
compared with the optimal algorithms. The advantageous performance of the
proposed defending mechanisms is verified in IEEE standard power system
testcases.Comment: Accepted for publication by IEEE Transactions on Smart Gri
The Cost of Mitigating Power Law Delay in Random Access Networks
Exponential backoff (EB) is a widely adopted collision resolution mechanism
in many popular random-access networks including Ethernet and wireless LAN
(WLAN). The prominence of EB is primarily attributed to its asymptotic
throughput stability, which ensures a non-zero throughput even when the number
of users in the network goes to infinity. Recent studies, however, show that EB
is fundamentally unsuitable for applications that are sensitive to large delay
and delay jitters, as it induces divergent second- and higher-order moments of
medium access delay. Essentially, the medium access delay follows a power law
distribution, a subclass of heavy-tailed distribution. To understand and
alleviate the issue, this paper systematically analyzes the tail delay
distribution of general backoff functions, with EB being a special case. In
particular, we establish a tradeoff between the tail decaying rate of medium
access delay distribution and the stability of throughput. To be more specific,
convergent delay moments are attainable only when the backoff functions
grows slower than exponential functions, i.e., when for all
. On the other hand, non-zero asymptotic throughput is attainable only
when backoff functions grow at least as fast as an exponential function, i.e.,
for some . This implies that bounded delay moments
and stable throughput cannot be achieved at the same time. For practical
implementation, we show that polynomial backoff (PB), where is a
polynomial that grows slower than exponential functions, obtains finite delay
moments and good throughput performance at the same time within a practical
range of user population. This makes PB a better alternative than EB for
multimedia applications with stringent delay requirements.Comment: 14 pages, 11 figure
Computation Rate Maximization for Wireless Powered Mobile-Edge Computing with Binary Computation Offloading
In this paper, we consider a multi-user mobile edge computing (MEC) network
powered by wireless power transfer (WPT), where each energy-harvesting WD
follows a binary computation offloading policy, i.e., data set of a task has to
be executed as a whole either locally or remotely at the MEC server via task
offloading. In particular, we are interested in maximizing the (weighted) sum
computation rate of all the WDs in the network by jointly optimizing the
individual computing mode selection (i.e., local computing or offloading) and
the system transmission time allocation (on WPT and task offloading). The major
difficulty lies in the combinatorial nature of multi-user computing mode
selection and its strong coupling with transmission time allocation. To tackle
this problem, we first consider a decoupled optimization, where we assume that
the mode selection is given and propose a simple bi-section search algorithm to
obtain the conditional optimal time allocation. On top of that, a coordinate
descent method is devised to optimize the mode selection. The method is simple
in implementation but may suffer from high computational complexity in a
large-size network. To address this problem, we further propose a joint
optimization method based on the ADMM (alternating direction method of
multipliers) decomposition technique, which enjoys much slower increase of
computational complexity as the networks size increases. Extensive simulations
show that both the proposed methods can efficiently achieve near-optimal
performance under various network setups, and significantly outperform the
other representative benchmark methods considered.Comment: This paper has been accepted for publication in IEEE Transactions on
Wireless Communication
Online Coordinated Charging Decision Algorithm for Electric Vehicles without Future Information
The large-scale integration of plug-in electric vehicles (PEVs) to the power
grid spurs the need for efficient charging coordination mechanisms. It can be
shown that the optimal charging schedule smooths out the energy consumption
over time so as to minimize the total energy cost. In practice, however, it is
hard to smooth out the energy consumption perfectly, because the future PEV
charging demand is unknown at the moment when the charging rate of an existing
PEV needs to be determined. In this paper, we propose an Online cooRdinated
CHARging Decision (ORCHARD) algorithm, which minimizes the energy cost without
knowing the future information. Through rigorous proof, we show that ORCHARD is
strictly feasible in the sense that it guarantees to fulfill all charging
demands before due time. Meanwhile, it achieves the best known competitive
ratio of 2.39. To further reduce the computational complexity of the algorithm,
we propose a novel reduced-complexity algorithm to replace the standard convex
optimization techniques used in ORCHARD. Through extensive simulations, we show
that the average performance gap between ORCHARD and the offline optimal
solution, which utilizes the complete future information, is as small as 14%.
By setting a proper speeding factor, the average performance gap can be further
reduced to less than 6%.Comment: 12 pages, 7 figure
Distributed Scheduling in Wireless Powered Communication Network: Protocol Design and Performance Analysis
Wireless powered communication network (WPCN) is a novel networking paradigm
that uses radio frequency (RF) wireless energy transfer (WET) technology to
power the information transmissions of wireless devices (WDs). When energy and
information are transferred in the same frequency band, a major design issue is
transmission scheduling to avoid interference and achieve high communication
performance. Commonly used centralized scheduling methods in WPCN may result in
high control signaling overhead and thus are not suitable for wireless networks
constituting a large number of WDs with random locations and dynamic
operations. To tackle this issue, we propose in this paper a distributed
scheduling protocol for energy and information transmissions in WPCN.
Specifically, we allow a WD that is about to deplete its battery to broadcast
an energy request buzz (ERB), which triggers WET from its associated hybrid
access point (HAP) to recharge the battery. If no ERB is sent, the WDs contend
to transmit data to the HAP using the conventional -persistent CSMA (carrier
sensing multiple access). In particular, we propose an energy queueing model
based on an energy decoupling property to derive the throughput performance.
Our analysis is verified through simulations under practical network
parameters, which demonstrate good throughput performance of the distributed
scheduling protocol and reveal some interesting design insights that are
different from conventional contention-based communication network assuming the
WDs are powered with unlimited energy supplies.Comment: This paper has been accepted for publication in 15th International
Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless
Networks (WiOpt 2017), Paris, France, 15th - 19th May, 201
Measurements and modeling of the effects of an orthogonal bias field on properties of isotropic magnetic materials
The effects of the orthogonal bias field were investigated systematically in this study. The measurements on various magnetic materials under different experimental conditions showed that the orthogonal bias field rotated the hysteresis loop reducing permeability and reduced its enclosed area. As a result, permeability, hysteresis loss, coercivity and remanence decreased with increasing orthogonal bias field. The experimental results indicated that the orthogonal field reduced hysteresis loss by increasing the component of reversible domain rotation magnetization;In the present study, the Jiles-Atherton model has been expanded to include the effect of an orthogonal bias field. Based on the experimental observation, a dynamically variable reversibility coefficient was proposed to include the reversible domain rotation magnetization in the model. This models the change of the reversibility coefficient during the magnetization process and is characterized by an irreversible field range. After incorporating an orthogonal anisotropy and the dynamic reversibility coefficient into the model, the modeled hysteresis loops showed all experimentally observed features and were consistent with the results of measurements on a ferrite toroid;Based on measurement results on a circular button ferrite inductor developed in this study, a prototype un-gapped variable ferrite inductor, which utilizes selected saturation to increase energy storage and can be controlled by an orthogonal current, was proposed in this study. The measurements on an assembled prototype rectangular button ferrite inductor based on the above design confirmed the expected behavior. The measured inductance was observed not only to decrease with increasing orthogonal current, but also with an appropriate choice of orthogonal current the inductance only has a small fluctuation within a designed excitation current range;Finite element method was extensively used to analyze and model problems involved in this study. 2D linear FEM modeling was used to evaluate the internal orthogonal field along a toroid axis and flux line distribution of magnetic devices, while 3D nonlinear FEM modeling was successfully used to study the evolution of the saturation regions of variable inductors and to model the terminal inductance of the variable inductors
User-Centric Joint Transmission in Virtual-Cell-Based Ultra-Dense Networks
In ultra-dense networks (UDNs), distributed radio access points (RAPs) are
configured into small virtual cells around mobile users for fair and
high-throughput services. In this correspondence, we evaluate the performance
of user-centric joint transmission (JT) in a UDN with a number of virtual
cells. In contrast to existing cooperation schemes, which assume constant RAP
transmit power, we consider a total transmit power constraint for each user,
and assume that the total power is optimally allocated to the RAPs in each
virtual cell using maximum ratio transmission (MRT). Based on stochastic
geometry models of the RAP and user locations, we resolve the correlation of
transmit powers introduced by MRT and derive the average user throughput.
Numerical results show that user-centric JT with MRT provides a high
signal-to-noise ratio (SNR) without generating severe interference to other
co-channel users. Moreover, we show that MRT precoding, while requiring
channel-state-information (CSI), is essential for the success of JT.Comment: Submitted to IEEE TVT correspondenc
Joint Spectrum Reservation and On-demand Request for Mobile Virtual Network Operators
With wireless network virtualization, Mobile Virtual Network Operators
(MVNOs) can develop new services on a low-cost platform by leasing virtual
resources from mobile network owners. In this paper, we investigate a two-stage
spectrum leasing framework, where an MVNO acquires radio spectrum through both
advance reservation and on-demand request. To maximize its surplus, the MVNO
jointly optimizes the amount of spectrum to lease in the two stages by taking
into account the traffic distribution, random user locations, wireless channel
statistics, Quality of Service (QoS) requirements, and the prices differences.
Meanwhile, the acquired spectrum resources are dynamically allocated to the
MVNO's mobile subscribers (users) according to fast channel fadings in order to
maximize the utilization of the resources. The MVNO's surplus maximization
problem is naturally formulated as a tri-level nested optimization problem that
consists of Dynamic Resource Allocation (DRA), on-demand request, and advance
reservation subproblems. To solve the problem efficiently, we rigorously
analyze the structure of the optimal solution in the DRA problem, and the
optimal value is used to find the optimal leasing decisions in the two stages.
In particular, we derive closed-form expressions of the optimal advance
reservation and on-demand requests when the proportional fair utility function
is adopted. We further extend the analysis to general utility functions and
derive a Stochastic Gradient Decent (SGD) algorithm to find the optimal leasing
decisions. Simulation results show that the two-stage spectrum leasing strategy
can take advantage of both the price discount of advance reservation and the
flexibility of on-demand request to deal with traffic variations.Comment: corrected typos; re-organise the presentation of the analytical
resul
Electrical Vehicle Charging Station Profit Maximization: Admission, Pricing, and Online Scheduling
The rapid emergence of electric vehicles (EVs) demands an advanced
infrastructure of publicly accessible charging stations that provide efficient
charging services. In this paper, we propose a new charging station operation
mechanism, the JoAP, which jointly optimizes the EV admission control, pricing,
and charging scheduling to maximize the charging station's profit. More
specifically, by introducing a tandem queueing network model, we analytically
characterize the average charging station profit as a function of the admission
control and pricing policies. Based on the analysis, we characterize the
optimal JoAP algorithm. Through extensive simulations, we demonstrate that the
proposed JoAP algorithm on average can achieve 330% and 531% higher profit than
a widely adopted benchmark method under two representative waiting-time penalty
rates.Comment: This paper has been submitted to IEEE Transactions on Sustainable
Energy for potential journal publicatio
- …